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Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer
Dhiraj Kapila1, Sarika Panwar2, M K Mohan Maruga Raja3
1Department of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India.
Abstract:
"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.
Insights
This study developed a deep learning system for accurate malignant mesothelioma (MM) diagnosis. Artificial intelligence, particularly neural networks, shows promise in identifying this asbestos-induced cancer early.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Malignant mesothelioma (MM) is a rare but aggressive cancer linked to asbestos exposure.
- Accurate diagnosis of MM is critical for effective treatment and has medicolegal importance.
- The epithelial/mesenchymal heterogeneity of MM poses diagnostic challenges.
Purpose of the Study:
- To develop an automated deep learning system for the medical diagnosis of malignant mesothelioma.
- To explore various artificial intelligence algorithms for reliable MM identification.
- To improve early detection and patient outcomes for malignant mesothelioma.
Main Methods:
- Investigated deep learning and traditional machine learning techniques.
- Selected algorithms include Support Vector Machine, Neural Network, and Decision Tree.
- Statistical analysis was performed using SPSS, focusing on Neural Network applications.
Main Results:
- The study explored the application of AI algorithms for malignant mesothelioma diagnosis.
- Neural Network analysis, utilizing SPSS, demonstrated potential in diagnosing MM.
- Deep learning systems offer a pathway for automated and accurate MM detection.
Conclusions:
- Deep learning systems can aid in the automatic diagnosis of malignant mesothelioma.
- AI, particularly neural networks, shows promise for early and accurate MM detection.
- Further development of these AI tools is crucial for improving patient survival rates.
